




'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     						=				logging.get_logger(__name__)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if "resnet-50" in model_name:
      A__       =     ResNetConfig.from_pretrained('microsoft/resnet-50'						)
   elif "resnet-101" in model_name:
      A__       =     ResNetConfig.from_pretrained('microsoft/resnet-101'						)
   else:
      raise ValueError('Model name should include either resnet50 or resnet101'						)

   A__       =     DetrConfig(use_timm_backbone=UpperCAmelCase      ,backbone_config=UpperCAmelCase						)

   # set label attributes
   A__       =     'panoptic' in model_name
   if is_panoptic:
      A__       =     250
   else:
      A__       =     91
      A__       =     'huggingface/label-files'
      A__       =     'coco-detection-id2label.json'
      A__       =     json.load(open(hf_hub_download(UpperCAmelCase      ,UpperCAmelCase      ,repo_type='dataset'						)      ,'r'						)						)
      A__       =     {int(UpperCAmelCase						): v for k, v in idalabel.items()}
      A__       =     idalabel
      A__       =     {v: k for k, v in idalabel.items()}

   return config, is_panoptic







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   # stem
   # fmt: off
   rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight')						)
   rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight')						)
   rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias')						)
   rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean')						)
   rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var')						)
   # stages
   for stage_idx in range(len(config.backbone_config.depths						)						):
      for layer_idx in range(config.backbone_config.depths[stage_idx]						):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
                )						)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
                )						)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
                )						)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
                )						)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
                )						)
         # 3 convs
         for i in range(3						):
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
                )						)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
                )						)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
                )						)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
                )						)
            rename_keys.append(
                (
                    F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
                    F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
                )						)
    # fmt: on

   for i in range(config.encoder_layers						):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (
              F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
              F"""encoder.layers.{i}.self_attn.out_proj.weight""",
          )						)
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")						)
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")						)
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")						)
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")						)
      rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")						)
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")						)
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")						)
      rename_keys.append(
          (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")						)
      rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")						)
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
              F"""decoder.layers.{i}.self_attn.out_proj.weight""",
          )						)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")						)
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
              F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
          )						)
      rename_keys.append(
          (
              F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
              F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
          )						)
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")						)
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")						)
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")						)
      rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")						)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")						)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")						)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")						)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")						)
      rename_keys.append(
          (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")						)
      rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")						)

   # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
   rename_keys.extend(
       [
           ('input_proj.weight', 'input_projection.weight'),
           ('input_proj.bias', 'input_projection.bias'),
           ('query_embed.weight', 'query_position_embeddings.weight'),
           ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
           ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
           ('class_embed.weight', 'class_labels_classifier.weight'),
           ('class_embed.bias', 'class_labels_classifier.bias'),
           ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
           ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
           ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
           ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
           ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
           ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
       ]						)

   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     state_dict.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     ''
   if is_panoptic:
      A__       =     'detr.'

   # first: transformer encoder
   for i in range(6						):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      A__       =     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
      A__       =     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[:256, :]
      A__       =     in_proj_bias[:256]
      A__       =     in_proj_weight[256:512, :]
      A__       =     in_proj_bias[256:512]
      A__       =     in_proj_weight[-256:, :]
      A__       =     in_proj_bias[-256:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6						):
      # read in weights + bias of input projection layer of self-attention
      A__       =     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
      A__       =     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""						)
      # next, add query, keys and values (in that order) to the state dict
      A__       =     in_proj_weight[:256, :]
      A__       =     in_proj_bias[:256]
      A__       =     in_proj_weight[256:512, :]
      A__       =     in_proj_bias[256:512]
      A__       =     in_proj_weight[-256:, :]
      A__       =     in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      A__       =     state_dict.pop(
          F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""						)
      A__       =     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""						)
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      A__       =     in_proj_weight_cross_attn[:256, :]
      A__       =     in_proj_bias_cross_attn[:256]
      A__       =     in_proj_weight_cross_attn[256:512, :]
      A__       =     in_proj_bias_cross_attn[256:512]
      A__       =     in_proj_weight_cross_attn[-256:, :]
      A__       =     in_proj_bias_cross_attn[-256:]







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						)

   return im







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=None      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__  ,			A__       =     get_detr_config(UpperCAmelCase						)

   # load original model from torch hub
   A__       =     {
       'detr-resnet-50': 'detr_resnet50',
       'detr-resnet-101': 'detr_resnet101',
   }
   logger.info(F"""Converting model {model_name}..."""						)
   A__       =     torch.hub.load('facebookresearch/detr'      ,model_name_to_original_name[model_name]      ,pretrained=UpperCAmelCase						).eval()
   A__       =     detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(UpperCAmelCase						):
      if is_panoptic:
         A__       =     'detr.' + src
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)
   # query, key and value matrices need special treatment
   read_in_q_k_v(UpperCAmelCase      ,is_panoptic=UpperCAmelCase						)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   A__       =     'detr.model.' if is_panoptic else 'model.'
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith('detr'						)
             and not key.startswith('class_labels_classifier'						)
             and not key.startswith('bbox_predictor'						)
         ):
            A__       =     state_dict.pop(UpperCAmelCase						)
            A__       =     val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            A__       =     state_dict.pop(UpperCAmelCase						)
            A__       =     val
         elif key.startswith('bbox_attention'						) or key.startswith('mask_head'						):
            continue
         else:
            A__       =     state_dict.pop(UpperCAmelCase						)
            A__       =     val
      else:
         if not key.startswith('class_labels_classifier'						) and not key.startswith('bbox_predictor'						):
            A__       =     state_dict.pop(UpperCAmelCase						)
            A__       =     val

    # finally, create HuggingFace model and load state dict
   A__       =     DetrForSegmentation(UpperCAmelCase						) if is_panoptic else DetrForObjectDetection(UpperCAmelCase						)
   model.load_state_dict(UpperCAmelCase						)
   model.eval()

   # verify our conversion on an image
   A__       =     'coco_panoptic' if is_panoptic else 'coco_detection'
   A__       =     DetrImageProcessor(format=UpperCAmelCase						)

   A__       =     processor(images=prepare_img()      ,return_tensors='pt'						)
   A__       =     encoding['pixel_values']

   A__       =     detr(UpperCAmelCase						)
   A__       =     model(UpperCAmelCase						)

   assert torch.allclose(outputs.logits      ,original_outputs['pred_logits']      ,atol=1e-3						)
   assert torch.allclose(outputs.pred_boxes      ,original_outputs['pred_boxes']      ,atol=1e-3						)
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks      ,original_outputs['pred_masks']      ,atol=1e-4						)
   print('Looks ok!'						)

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""						)
      Path(UpperCAmelCase						).mkdir(exist_ok=UpperCAmelCase						)
      model.save_pretrained(UpperCAmelCase						)
      processor.save_pretrained(UpperCAmelCase						)

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info('Uploading PyTorch model and image processor to the hub...'						)
      model.push_to_hub(F"""nielsr/{model_name}"""						)
      processor.push_to_hub(F"""nielsr/{model_name}"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()

    parser.add_argument(
        '''--model_name''',
        default='''detr-resnet-50''',
        type=str,
        choices=['''detr-resnet-50''', '''detr-resnet-101'''],
        help='''Name of the DETR model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
    )
    parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
    lowerCAmelCase_     						=				parser.parse_args()
    convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)

@dataclass
class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Union[List[PIL.Image.Image], np.ndarray]
 __snake_case:							Optional[List[bool]]
 __snake_case:							Optional[List[bool]]


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipeline_if import IFPipeline
    from .pipeline_if_imgaimg import IFImgaImgPipeline
    from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
    from .pipeline_if_inpainting import IFInpaintingPipeline
    from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
    from .pipeline_if_superresolution import IFSuperResolutionPipeline
    from .safety_checker import IFSafetyChecker
    from .watermark import IFWatermarker


